标签: tsne

我想将 T-sne 功能用于 DBSCAN 聚类算法，但 sklearn 实现未针对 n_components>4 运行。

from sklearn.manifold import TSNE
X = np.array([[0, 0, 0,2, 0, 0,2], [0, 1, 1,53, 0, 0,2], [1, 0, 1,12, 0, 0,2], [1, 1, 1,75, 0, 0,2]])
X_embedded = TSNE(n_components=5).fit_transform(X)

错误：

ValueError                                Traceback (most recent call last)
<ipython-input-22-79c671f39a06> in <module>
----> 1 tsne_data = model.fit(clustering_ready_data_encoded)

~/anaconda3/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py in fit(self, X, y)
    902         y : Ignored
    903         """
--> 904         self.fit_transform(X)
    905         return self

~/anaconda3/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py in fit_transform(self, X, y)
    884             Embedding of the training …

我正在尝试在张量流中实现 t-SNE 可视化以执行图像分类任务。我主要在网上找到的都已经在Pytorch中实现了。看这里。

这是我用于训练目的的通用代码，它工作得很好，只是想向其中添加 t-SNE 可视化：

import pandas as pd
import numpy as np
import tensorflow as tf
import cv2
from tensorflow import keras
from tensorflow.keras import layers, Input
from tensorflow.keras.layers import Dense, InputLayer, Flatten
from tensorflow.keras.models import Sequential, Model
from  matplotlib import pyplot as plt
import matplotlib.image as mpimg
from PIL import Image
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

.
.
.

base_model=tf.keras.applications.ResNet152(
    include_top=False, weights='imagenet', input_tensor=None,
    input_shape=None, pooling=None)

.
.
.

base_model.trainable = False


# Create …